Ontology Engineering Methodology Research Articles

An Ontology-Based Decision Support System for Tailored Clinical Nutrition Recommendations for Patients With Chronic Obstructive Pulmonary Disease: Development and Acceptability Study.

Chronic obstructive pulmonary disease (COPD) is a chronic condition among the main causes of morbidity and mortality worldwide, representing a burden on health care systems. Scientific literature highlights that nutrition is pivotal in respiratory inflammatory processes connected to COPD, including exacerbations. Patients with COPD have an increased risk of developing nutrition-related comorbidities, such as diabetes, cardiovascular diseases, and malnutrition. Moreover, these patients often manifest sarcopenia and cachexia. Therefore, an adequate nutritional assessment and therapy are essential to help individuals with COPD in managing the progress of the disease. However, the role of nutrition in pulmonary rehabilitation (PR) programs is often underestimated due to a lack of resources and dedicated services, mostly because pneumologists may lack the specialized training for such a discipline. This work proposes a novel knowledge-based decision support system to support pneumologists in considering nutritional aspects in PR. The system provides clinicians with patient-tailored dietary recommendations leveraging expert knowledge. The expert knowledge-acquired from experts and clinical literature-was formalized in domain ontologies and rules, which were developed leveraging the support of Italian clinicians with expertise in the rehabilitation of patients with COPD. Thus, by following an agile ontology engineering methodology, the relevant formal ontologies were developed to act as a backbone for an application targeted at pneumologists. The recommendations provided by the decision support system were validated by a group of nutrition experts, whereas the acceptability of such an application in the context of PR was evaluated by pneumologists. A total of 7 dieticians (mean age 46.60, SD 13.35 years) were interviewed to assess their level of agreement with the decision support system's recommendations by evaluating 5 patients' health conditions. The preliminary results indicate that the system performed more than adequately (with an overall average score of 4.23, SD 0.52 out of 5 points), providing meaningful and safe recommendations in compliance with clinical practice. With regard to the acceptability of the system by lung specialists (mean age 44.71, SD 11.94 years), the usefulness and relevance of the proposed solution were extremely positive-the scores on each of the perceived usefulness subscales of the technology acceptance model 3 were 4.86 (SD 0.38) out of 5 points, whereas the score on the intention to use subscale was 4.14 (SD 0.38) out of 5 points. Although designed for the Italian clinical context, the proposed system can be adapted for any other national clinical context by modifying the domain ontologies, thus providing a multidisciplinary approach to the management of patients with COPD.

CHEKG: a collaborative and hybrid methodology for engineering modular and fair domain-specific knowledge graphs

Ontologies constitute the semantic model of Knowledge Graphs (KGs). This structural association indicates the potential existence of methodological analogies in the development of ontologies and KGs. The deployment of fully and well-defined methodologies for KG development based on existing ontology engineering methodologies (OEMs) has been suggested and efficiently applied. However, most of the modern/recent OEMs may not include tasks that (i) empower knowledge workers and domain experts to closely collaborate with ontology engineers and KG specialists for the development and maintenance of KGs, (ii) satisfy special requirements of KG development, such as (a) ensuring modularity and agility of KGs, (b) assessing and mitigating bias at schema and data levels. Toward this aim, the paper presents a methodology for the Collaborative and Hybrid Engineering of Knowledge Graphs (CHEKG), which constitutes a hybrid (schema-centric/top-down and data-driven/bottom-up), collaborative, agile, and iterative approach for developing modular and fair domain-specific KGs. CHEKG contributes to all phases of the KG engineering lifecycle: from the specification of a KG to its exploitation, evaluation, and refinement. The CHEKG methodology is based on the main phases of the extended Human-Centered Collaborative Ontology Engineering Methodology (ext-HCOME), while it adjusts and expands the individual processes and tasks of each phase according to the specialized requirements of KG development. Apart from the presentation of the methodology per se, the paper presents recent work regarding the deployment and evaluation of the CHEKG methodology for the engineering of semantic trajectories as KGs generated from unmanned aerial vehicles (UAVs) data during real cultural heritage documentation scenarios.

A novel agile ontology engineering methodology for supporting organizations in collaborative ontology development

Ontologies can represent technological enablers for knowledge elicitation and management in different kinds of organizations, especially with the exponential growth of sources and types of data fostered by digital transformation. However, their adoption in business applications is still limited, with existing Ontology Engineering Methodologies (OEMs) lacking adequate support during knowledge elicitation, authoring and reuse phases. This paper introduces a novel agile ontology engineering methodology (AgiSCOnt) to support ontologists (especially novice ones) in ontology development workflow, fostering collaboration with domain experts in an iterative, flexible and customizable approach. AgiSCOnt combines macro-level instructions with micro-level guidance, leveraging existing techniques and a management framework to help novice ontologists throughout the whole ontology engineering process. The methodology is compared to existing OEMs and assessed with three other agile methodologies (UPONLite, SAMOD, and RapidOWL). The evaluation is conducted with a sample of novice ontologists in a learning environment on Industry 4.0 technologies. Both the development process with a methodology from a user perspective and the quality of the developed ontologies were considered in the evaluation. Preliminary results show that AgiSCOnt effectively supports authoring and reuse, with developed ontologies of good quality. It is perceived as clear and simple, while being flexible and adaptable enough, thus supporting knowledge management and sharing in industrial organizations through the documentation of the ontologies.

OntoSuSD: Software engineering approaches integration ontology for sustainable software development

AbstractDifferent software development approaches (SDAs) are developed with broad portfolios of development processes. Each of the approaches has certain exclusive principles, practices, thinking, and values, which are informally represented, implemented, and improperly institutionalized. Ontologies are developed for the representation, assessment, and adaptation of SDAs separately without having a shared terminology which may lead to terminological conflict and confusion affecting the simultaneous representation and implementation in software development industry and academia. The software engineering approaches does not consider and support sustainability as priority concern. However, the approaches have capabilities of supporting sustainable software development in different sustainability aspects. This research article aims for the designing and development of an integrated ontology of software engineering approaches (i.e., agile, lean, and green) named OntoSuSD (ontology for sustainable software development) to support sustainable software development knowledge, awareness, and implementation. The goal of OntoSuSD is to propose, design and develop a formal, generic, consistent, and shared knowledge base containing semantic terminology and description of concepts and relationships generated around the representation and implementation of lean, agile, and green approaches in software development processes, which will facilitate their simultaneous implementation and assessment for sustainable software development. The OntoSuSD is developed using practical ontology engineering methodology by reusing relevant ontologies and explicit concepts and properties are defined to fulfill knowledge requirements and representations of the domain. The OntoSuSD is evaluated, and results infer OntoSuSD has high ontological design, good domain coverage, potential applications and achieves purpose of the ontology development.

An ontology-based, general-purpose and Industry 4.0-ready architecture for supporting the smart operator (Part I – Mixed reality case)

The advent of novel industry 4.0-driven technologies is offering significant opportunities to manufacturing systems, but at the same time it is posing new great challenges. The growing number of connected and interconnected devices is enormously increasing the amount of data generated, which must be properly organized to give value to the business. Basically, the need for approaches that are able to guarantee compliance with FAIR data principles is significantly emerging. Recently, the KNOW4I platform has been proposed in the literature to support the smart operator through a suite of Smart Utilities and Objects (Longo et al., 2022). The main purpose of this paper is to extend such platform, in the form of an ontology-based, general-purpose and industry 4.0-ready architecture, capable of improving the capabilities of the smart operator, with a focus on mixed reality. The novel proposal is based on two fundamental aspects: (1) a new general ontology, developed through the ontology engineering methodology; (2) the adoption of FIWARE, an open-source infrastructure, capable of enabling interoperability between different systems. The proposed architecture is implemented and validated on two case studies belonging to the manufacturing sector, which respectively concern (1) scheduled maintenance and alarm management and (2) customer order management. The experimental phase shows that the architecture is able to effectively and efficiently support the smart operator.

An evaluation of agile Ontology Engineering Methodologies for the digital transformation of companies

Ontologies are increasingly recognised among the key enablers of the digital transformation of knowledge management processes, but still with a low level of adoption in manufacturing companies. Because ontologies and underlying technologies are complex, Ontology Engineering Methodologies (OEMs) provide a set of guidelines to move from an informal to a formal representation of the company’s knowledge base. This study evaluates three agile OEMs, i.e. UPONLite, SAMOD and RapidOWL, in terms of their process and outcome features, i.e. the OEM steps and the expected quality of the ontological models produced. The assessment is performed from the viewpoint of developers of ontology-based technologies in real industrial use cases. Results show that the three agile OEMs reflect different features to effectively support the digital transformation of companies' knowledge management; thus, they cannot be interchangeable. UPONLite is more effective in contexts where there is a lack of skills in OE, with the need for a structured approach in involving domain experts and generating documentation. SAMOD requires a more extended development period, but with several cycles that allow to map different types of knowledge and enable a “try-and-learn” approach. Conversely, RapidOWL lacks a structured sequence of modelling activities and encourages developers to be creative, but at the same time requires higher expertise in OE. Thus, companies and personnel dedicated to OE should choose the methodology according to the main aims guiding their digitalisation process, the current development status, and the level of expertise.

ReqTagger: A Rule-Based Tagger for Automatic Glossary of Terms Extraction from Ontology Requirements

Abstract Glossary of Terms extraction from textual requirements is an important step in ontology engineering methodologies. Although initially it was intended to be performed manually, last years have shown that some degree of automatization is possible. Based on these promising approaches, we introduce a novel, human interpretable, rule-based method named ReqTagger, which can extract candidates for ontology entities (classes or instances) and relations (data or object properties) from textual requirements automatically. We compare ReqTagger to existing automatic methods on an evaluation benchmark consisting of over 550 requirements and tagged with over 1700 entities and relations expected to be extracted. We discuss the quality of ReqTagger and provide details showing why it outperforms other methods. We also publish both the evaluation dataset and the implementation of ReqTagger.

F-ONTOCOM: A Fuzzified Cost Estimation Approach for Ontology Engineering

Estimating effort is an essential prerequisite for the wide-scale dispersal of ontologies. Not much attention has yet been paid to this essential aspect of ontology building. To date, ONTOCOM is the most prominent model for ontology cost estimation. Many factors influencing the building cost of an ontology are depicted by linguistic terms like Very High, High, . . . and so on; making them vague and indistinct. This fuzziness is quite uncertain and must be taken into consideration. The available effort estimation models do not consider the uncertainty of fuzziness. In this work, we propose an effort estimation methodology for ontology engineering using Fuzzy Logic i.e. F-ONTOCOM (Fuzzy-ONTOCOM) to overcome of uncertainty and imprecision. We have defined the corresponding Fuzzy sets for each effort multiplier and its associated linguistic value, and represented the same by triangular membership functions. F-ONTOCOM is applied to a dataset of 148 ontology projects and evaluated over various evaluation criteria. FONTOCOM outperforms the existing effort-estimation models; it has been concluded that F-ONTOCOM improves the cost estimation accuracy and estimated cost is very close to actual cost.

Ontologies: a revamped cross-disciplinary buzzword or a truly promising interdisciplinary research topic?

The following article provides an introductory overview of the different research domains (computational linguistics, termino graphy, artificial intelligence (AI), philosophy and database semantics) for which ontologies and the emerging field of the Semantic Web have become a main point of interest. It will be pointed out that each of these domains uses a different definition for an ontology. A specific ontology engineering methodology (VUB STAR Lab DOGMA) will be presented and emphasis will be put on the specific role and contribution of (multilingual) terminography in this ontology. In addition, we will explain what ontologies might offer to advance the state of the art of linguistics and terminography.

Collaborative Ontology Engineering Methodologies for the Development of Decision Support Systems: Case Studies in the Healthcare Domain

New models and technological advances are driving the digital transformation of healthcare systems. Ontologies and Semantic Web have been recognized among the most valuable solutions to manage the massive, various, and complex healthcare data deriving from different sources, thus acting as backbones for ontology-based Decision Support Systems (DSSs). Several contributions in the literature propose Ontology engineering methodologies (OEMs) to assist the formalization and development of ontologies, by providing guidelines on tasks, activities, and stakeholders’ participation. Nevertheless, existing OEMs differ widely according to their approach, and often lack of sufficient details to support ontology engineers. This paper performs a meta-review of the main criteria adopted for assessing OEMs, and major issues and shortcomings identified in existing methodologies. The key issues requiring specific attention (i.e., the delivery of a feasibility study, the introduction of project management processes, the support for reuse, and the involvement of stakeholders) are then explored into three use cases of semantic-based DSS in health-related fields. Results contribute to the literature on OEMs by providing insights on specific tools and approaches to be used when tackling these issues in the development of collaborative OEMs supporting DSS.

OntoPhaco: An Ontology for Virtual Reality Training in Ophthalmology Domain—A Case Study of Cataract Surgery

Performing a safe and successful cataract surgery greatly depends on the effective training programs of the residents that are commonly offered by several public and private healthcare providers. Virtual Reality Training (VRT) as a training tool for example, expresses potential capabilities to improve the learning curve, increasing trainee confidence and acquisition of skills. However, the success of this tool highly depends on how close this tool is to the reality. Ontology is called as a significant modelling tool which can help to provide a shared and common understanding of a domain, and in this scenario, a standardized terminology for representing the training domain, and actions taking place in Virtual Environment (VE). Recent Systematic Literature Review (SLR) findings show that most ontology designs for VRT are not built systematically and there is no ontology describing the domain knowledge for VRT in the ophthalmology field. There is also a lack of a high achievement rate in implementing ontology driven VRT, calls for systematic and comprehensive steps. Therefore, in order to lower the failure rate of ontology applied to VRT, OntoPhaco was designed and developed based on philosophically grounded foundational ontologies, namely, Unified Foundational Ontology (UFO) and Design & Engineering Methodology for Organizations (DEMO). The evaluation results showed that the use of OntoPhaco was able to improve the student’s learning experience, assist effectively in building VR training scenarios, and lead to a successful VRT processes. The lessons learnt from designing OntoPhaco for this domain could generally generate valuable contribution to the theory and practice of VRT, knowledge management and ontology engineering in complex domains.

Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations

Abstract The aim of this critical review paper is threefold: (a) to provide an insight on the impact of ontology engineering methodologies (OEMs) to the evolution of living and reused ontologies, (b) to update the ontology engineering (OE) community on the status and trends in OEMs and of their use in practice and (c) to propose a set of recommendations for working ontologists to consider during the life cycle of living, evolved and reused ontologies. The work outlined in this critical review paper has been motivated by the need to address critical issues on keeping ontologies alive and evolving while these are shared in wide communities. It is argued that the engineering of ontologies must follow a well-defined methodology, addressing practical aspects that would allow (sometimes wide) communities of experts and ontologists to reach consensus on developments and keep the evolution of ontologies ‘in track’. In doing so, specific collaborative and iterative tool-supported tasks and phases within a complete and evaluated ontology life cycle are necessary. This way the engineered ontologies can be considered ‘shared, commonly agreed and continuously evolved “live” conceptualizations’ of domains of discourse. Today, in the era of Linked Data and Knowledge Graphs, it is more necessary than ever not to neglect to consider the recommendations that OEMs explicitly and implicitly introduce and their implications to the evolution of living ontologies. This paper reports on the status of OEMs, identifies trends and provides recommendations based on the findings of an analysis that concerns the impact of OEMs to the status of well-known, widely used and representative ontologies.

Analysis of Ontology Competency Questions and their formalizations in SPARQL-OWL

Competency Questions (CQs) are natural language questions outlining and constraining the scope of knowledge represented in an ontology. Despite that CQs are a part of several ontology engineering methodologies, the actual publication of CQs for the available ontologies is very limited and even scarcer is the publication of their respective formalizations in terms of, e.g., SPARQL queries. This paper aims to contribute to addressing the myriad of engineering hurdles to using CQs in ontology development. A prerequisite to this is to understand the relation between CQs and the queries over the ontology. We use a new dataset of 234 competency questions and their SPARQL-OWL queries for several ontologies in different domains developed by different groups, and analysed the CQs in two principal ways. The first stage focused on a linguistic analysis of the natural language text itself, i.e., a lexico-syntactic analysis without any presuppositions of ontology elements, and a subsequent step of semantic analysis in order to find patterns. This increased diversity of CQ sources resulted in a 4-5-fold increase of hitherto published patterns, to 106 distinct CQ patterns, which have a limited subset of few patterns shared across the CQ sets from the different ontologies. Next, we analysed the relation between the found CQ patterns and their respective SPARQL-OWL patterns, which revealed that one CQ pattern may be realized by more than one SPARQL-OWL query pattern, and vice versa. These insights may contribute to establishing common practices, templates, automation, and user tools that will support CQ formulation, formalization, execution, and general management.

OntoGamif: A modular ontology for integrated gamification

Gamification is known as the use of game design elements in non-game contexts. It has gained a great interest from both industrials and academics during the last years due to its role in increasing engagement of people in several sectors including business, learning, crowdsourcing and enterprise in formation systems. However, due to its inherent multi-disciplinarity, gamification lacks an integrated view and a holistic and consensual agreement on its foundational concepts among the stakeholders. In this context we propose OntoGamif (Ontology of Gamification), a modular ontology for the gamification domain of interest covering topics concerning, for instance, target users, organizational structures, ethical issues, and psychological factors. These are organized as seven linked modular sub-ontologies that can be used also independently to support the work of gamification designers implementing personalized gamified solutions. The process used to build OntoGamif was inspired by some of the most used ontology engineering methodologies. Moreover, we discuss the multi-perspective approach in evaluating OntoGamif including experts’ validation, case study analysis, comparison to a corpus and a mapping of OntoGamif to the SUMO upper level ontology.

Analysis of Ontology Competency Questions and their Formalisations in SPARQL-OWL

Competency Questions (CQs) are natural language questions outlining and constraining the scope of knowledge represented in an ontology. Despite that CQs are a part of several ontology engineering methodologies, the actual publication of CQs for the available ontologies is very limited and even scarcer is the publication of their respective formalisations in terms of, e.g., SPARQL queries. This paper aims to contribute to addressing the myriad of engineering hurdles to using CQs in ontology development. A prerequisite to this is to understand the relation between CQs and the queries over the ontology. We use a new dataset of 234 competency questions and their SPARQL-OWL queries for several ontologies in dierent domains developed by dierent groups, and analysed the CQs in two principal ways. The first stage focused on a linguistic analysis of the natural language text itself, i.e., a lexico-syntactic analysis without any presuppositions of ontology elements, and a subsequent step of semantic analysis in order to find patterns. This increased diversity of CQ sources resulted in a 4-5-fold increase of hitherto published patterns, to 106 distinct CQ patterns, which have a limited subset of few patterns shared across the CQ sets from the dierent ontologies. Next, we analysed the relation between the found CQ patterns and their respective SPARQL-OWL patterns, which revealed that one CQ pattern may be realised by more than one SPARQL-OWL query pattern, and vice versa. These insights may contribute to establishing common practices, templates, automation, and user tools that will support CQ formulation, formalisation, execution, and general management.

The development of an ontology for describing the capabilities of manufacturing resources

Today’s highly volatile production environments call for adaptive and rapidly responding production systems that can adjust to the required changes in processing functions, production capacity and dispatching of orders. There is a desire to support such system adaptation and reconfiguration with computer-aided decision support systems. In order to bring automation to reconfiguration decision making in a multi-vendor resource environment, a common formal resource model, representing the functionalities and constraints of the resources, is required. This paper presents the systematic development process of an OWL-based manufacturing resource capability ontology (MaRCO), which has been developed to describe the capabilities of manufacturing resources. As opposed to other existing resource description models, MaRCO supports the representation and automatic inference of combined capabilities from the representation of the simple capabilities of co-operating resources. Resource vendors may utilize MaRCO to describe the functionality of their offerings in a comparable manner, while the system integrators and end users may use these descriptions for the fast identification of candidate resources and resource combinations for a specific production need. This article presents the step-by-step development process of the ontology by following the five phases of the ontology engineering methodology: feasibility study, kickoff, refinement, evaluation, and usage and evolution. Furthermore, it provides details of the model’s content and structure.

Developing and Validating the Socio-Technical Model in Ontology Engineering

This paper describes results from an attempt to develop a model in ontology engineering methodology and a way to validate the model. The approach to methodology in ontology engineering is from the point view of socio-technical system theory. Qualitative research synthesis is used to build the model using meta-ethnography. In order to ensure the objectivity of the measurement, inter-rater reliability method was applied using a multi-rater Fleiss Kappa. The results show the accordance of the research output with the diamond model in the socio-technical system theory by evidence of the interdependency of the four socio-technical variables namely people, technology, structure and task.

BIM – New rules of measurement ontology for construction cost estimation

For generations, the process of cost estimation has been manual, time-consuming and error-prone. Emerging Building Information Modelling (BIM) can exploit standard measurement methods to automate cost estimation process and improve inaccuracies. Structuring standard measurement methods in an ontologically and machine readable format for a BIM software can greatly facilitate the process of improving inaccuracies in cost estimation. This study explores the development of an ontology based on New Rules of Measurement (NRM) for cost estimation during the tendering stages. The methodology adopted is methontology, one of the most widely used ontology engineering methodologies. To ensure the ontology is fit for purpose, cost estimation experts are employed to check the semantics, descriptive logic-based reasoners are used to syntactically check the ontology and a leading 4D BIM modelling software is used on a case study building to test/validate the proposed ontology.

An ontology development approach using concept maps driven by automatic term extraction

A number of ontology engineering methodologies have been proposed to date. Distinct methodologies rely on different techniques and activities for developing ontologies. The concept mapping technique has been recently used for developing ontologies related to different domains. However, the existing approaches using concept mapping do not make use of any automatic term extraction process to ease the overall ontology development process and their evaluation procedures are not robust to specifically meet the need for ontology designs emerging from concept maps. This paper proposes a new approach which uses the concept mapping technique coupled with a term extraction engine. The engine automatically extracts the candidate terms from the competency questions which reduces ontology development time and efforts. Furthermore, considering the importance of evaluation, a robust evaluation procedure is proposed which validates the ontologies at both the formal level and the graphical level.

An ontology development approach using concept maps driven by automatic term extraction

A number of ontology engineering methodologies have been proposed to date. Distinct methodologies rely on different techniques and activities for developing ontologies. The concept mapping technique has been recently used for developing ontologies related to different domains. However, the existing approaches using concept mapping do not make use of any automatic term extraction process to ease the overall ontology development process and their evaluation procedures are not robust to specifically meet the need for ontology designs emerging from concept maps. This paper proposes a new approach which uses the concept mapping technique coupled with a term extraction engine. The engine automatically extracts the candidate terms from the competency questions which reduces ontology development time and efforts. Furthermore, considering the importance of evaluation, a robust evaluation procedure is proposed which validates the ontologies at both the formal level and the graphical level.